Sequencing AI Automation and Data Interoperability in Oncology Using a Scenario-Planning Framework Coupled With Discrete-Event Simulation: Proof-of-Concept Study

Clinical Snapshot

Key Findings

• P Value: Not reported for this simulation study

• Effect Size: Fully integrated scenario reduced RTTI by 50% (14.9 vs 37.1 days) and nearly doubled throughput (1244 vs 647 patients/year)

• Primary Outcome: Referral-to-treatment interval (RTTI) and patient throughput across four AI adoption scenarios

• Nnt Or Sensitivity: Isolated automation increased RTTI by 26% compared to baseline, demonstrating importance of coordinated implementation

• Confidence Interval: Standard deviations provided: RTTI SD 0.3-1.3 days, throughput SD 10.1-21.4 patients/year

Clinical Application

Framework appears feasible for strategic planning but requires significant modeling expertise and local parameter estimation Highly relevant for Australian cancer services planning, particularly given recent investments in digital health infrastructure and the need to optimize cancer care pathways within public health systems Oncology departments and health systems planning digital transformation initiatives

Abstract

BACKGROUND: As oncology workflows integrate increasingly autonomous artificial intelligence (AI) agents, health systems face uncertainty regarding operational impacts. Traditional linear forecasting methods fail to capture second-order effects such as governance saturation, induced demand, and bottleneck migration. To navigate this complexity, the emerging field of medical futures studies requires methodologies that bridge qualitative strategic foresight with quantitative operational modeling. These system-level dynamics directly influence timely diagnosis, treatment delays, and overall health system resilience. OBJECTIVE: This study aimed to develop a proof-of-concept framework coupling qualitative scenario planning with computational discrete-event simulation to stress-test oncology AI adoption strategies. METHODS: We defined a strategic state space using 2 orthogonal axes, AI automation intensity and data interoperability, resulting in 4 distinct futures scenarios. We translated these qualitative narratives into a quantitative discrete-event simulation model of a 3-year operational horizon. The model quantified system performance (referral-to-treatment interval [RTTI] and throughput), volatility, and resource constraints across different adoption trajectories. RESULTS: The scenario-planning phase yielded 4 operational archetypes (analog oncology, automation islands, interconnected clinicians, and AI-orchestrated care) with distinct constraints, risks, and failure modes. In the simulation, the fully integrated scenario maximized capacity (1244, SD 21.4 patients per year) and halved the mean RTTI to 14.9 (SD 0.3) days, a magnitude comparable to major pathway redesign interventions. Isolated automation without data infrastructure led to reduced system performance, increasing RTTI by 26% (37.1, SD 1.3 days) and reducing throughput to 647 (SD 10.1) patients per year due to administrative governance saturation. The model illustrated a structural bottleneck migration: successful upstream AI adoption shifted binding constraints from diagnostic scanners to downstream chemotherapy infusion units, whereas missing data interoperability resulted in governance constraints. Pathway optimization analysis indicated that a coordinated strategy prioritizing early improvements in data interoperability reduced transition volatility compared to an automation-first approach. CONCLUSIONS: Integrating qualitative scenario planning with quantitative simulations enabled a systematic evaluation of oncology AI adoption strategies. As a proof of concept, it offers a replicable framework for health leaders to model future scenarios of digital transformation in times of high uncertainty. Subsequent work should expand this methodology to incorporate financial and health equity dimensions, establishing simulation-based scenario planning as an important tool in medical futures studies.

References

1. 1.May, P., Brookman-May, S. D., Garrahy, E., & von Büren, J. (2026). Sequencing AI automation and data interoperability in oncology using a scenario-planning framework coupled with discrete-event simulation: Proof-of-concept study. Journal of Medical Internet Research, 28(5), e92642. https://doi.org/10.2196/92642